Metro stations are considered high-quality resources for promoting urban development, which have great influences on the surrounding land use changes. The simulation and prediction of land use change can provide a scientific basis for urban land planning. In this work, the cellular automata (CA)-Markov model was adopted by taking into account point of interest (POI) kernel density and station accessibility as driving factors to predict the land use change of station surrounding areas. Then, the land type compositions of different years, temporal and spatial evolution of landscape patterns, and strategies of different metro stations were explored. The results show that the Kappa coefficients of the Zoo Station and the Lu Xiao Station are 87% and 79%, respectively, indicating that the improved CA-Markov model can predict land use changes more accurately by considering POI kernel density and station accessibility. Finally, different optimized strategies based on systematic predictions of land use landscape patterns according to the spatial and temporal distribution of metro stations were proposed. The work provides important references for predicting the impact of new metro stations on land use in the future and guides the adjustment and optimization of land use policy planning.

Urbanization promotes the development of the subway system, and the particulate matter (PM) concentrations inside have received increasing attention. This study first measured the dynamic PM_2.5 and PM₁₀ concentrations in a metro carriage in Chengdu and explored the dominant influencing factors. The personal inhalation intakes of different routes were evaluated. The results showed that the in-carriage PM_2.5 and PM₁₀ concentrations ranged from 11 to 74 μg/m³ (mean: 36.7 μg/m³) and 13 to 89 μg/m³ (mean: 40.1 μg/m³), respectively. When the train passed from the overground to underground, the in-carriage PM_2.5 and PM₁₀ concentrations increased by 30.4% and 32.9%, respectively. No specific linear relationship between passenger number and in-carriage PM concentrations was found. In-carriage PM concentrations decreased after the carriage doors were opened on the platforms. PM_2.5 inhalation intakes ranged from 1.08 to 9.52, with a mean of 4.24 µg. For the passengers with the same age and sex, the average inhaled PM_2.5 intake in the metro carriage on the route with more underground platforms was higher. This study not only revealed the PM characteristics in the Chengdu metro system for the first time, but also provided guidelines for reducing the in-carriage PM concentrations to build a healthier travel environment.

Over the recent years, the energy use of artificial lighting has been gradually increasing in office buildings. Tubular daylight guidance system (TDGS) is an innovative daylighting technology, which is used to transport daylight into the core of a building. In this study, the effects of the tube radius of TDGSs, window-to-wall ratio (WWR), and window orientation on the indoor illuminance, artificial lighting electricity consumption, and heating/cooling loads of an office room are investigated for the five climate zones in China by both numerical simulations and experiments. The model is developed using Radiance and Ecotect, and its feasibility is validated by comparing the simulated indoor illuminance distributions with the field measurements. The results demonstrate that the increase in the tube radius and the decrease in the WWR and the utilization of a north-facing window can improve the daylighting performance and energy savings of the TDGSs. In general, the TDGSs can reduce the artificial lighting electricity consumption and heating load while increasing the cooling load of the office room. Due to the implementation of TDGSs, the maximum potential energy savings (on the lighting energy use and the heating/cooling energy use) are 27.6% in Kunming (moderate climate), 13.5% in Chengdu (hot summer and cold winter), 10.6% in Guangzhou (hot summer and warm winter), 10.3% in Beijing (cold), and 5.0% in Harbin (severe cold).